KR20170031829A

KR20170031829A - Method and system for target acquisition and tracking using marine radar

Info

Publication number: KR20170031829A
Application number: KR1020150128909A
Authority: KR
Inventors: 백운식; 이희용
Original assignee: 금호마린테크 (주)
Priority date: 2015-09-11
Filing date: 2015-09-11
Publication date: 2017-03-22

Abstract

The present invention relates to a method of acquiring a timetable using a ship radar and a system for acquiring a timetable using the same, and a technical object of the present invention is to provide a system and method for acquiring timetable, It acquires the target through the sequential image processing such as Gaussian filter application, binarization and labeling, determines the observation position of the target through the near region search method, and analyzes the behavior of the target through the Kalman filter with the system model of constant velocity motion To be able to.
For this purpose, an embodiment of the present invention provides a method of acquiring a target in a track tracking system using a ship radar, the method comprising: extracting a target using a predetermined sequential image processing technique in a radar still image formed by the ship radar, The sequential image processing technique discloses a method of acquiring a target image using a ship radar that sequentially performs a grayscale conversion process, a Gaussian filter process, a binarization process, and a labeling process on the radar still image.

Description

TECHNICAL FIELD [0001] The present invention relates to a method of acquiring a ship using a ship radar and a system for acquiring a ship using the ship radar,

An embodiment of the present invention relates to a method for acquiring a target in a target tracking system using a ship radar and a system for acquiring a target using the target.

In general, a Marine Radar is a radar device designed for use in marine vessels. It is used to detect and position the marine obstacles, transit vessels, and coastal features during operation, It is one of the main navigation equipment of a ship that supports safe navigation at sea used for the purpose.

At this time, the radar is a device for measuring the distance and direction to the object by receiving the reflected electromagnetic wave by emitting the electromagnetic wave, and information such as the distance, direction and altitude of the target is scanned in the PPI (Plan Position Indicator) Respectively. The radar is divided into aerial radar, marine radar, weather radar, and tracking radar depending on technical specifications such as frequency used, beam width, and purpose of use.

Generally, in the field of CCTV image processing, a target is referred to as a target, and a search for a meaningful object in an image is called extraction.

FIG. 1 is a diagram illustrating a method for tracking a target according to the prior art.

As shown in FIG. 1, the conventional track tracking method extracts a border line from a CCTV image or extracts a target mark using a technique such as histogram analysis, and performs a Cam Shift, a Particle Filter ) Is a common method of image tracking.

The main functions of the marine radar are location confirmation and collision avoidance. The main concern for this is the maritime navigation ship and maritime navigation sign.

Published Patent Publication No. 2003-0045868 'Ship tracking information satellite transmission method' Patent Publication No. 10-2009-0091449 'Tracking Target Determination Method of Ship Radar Tracking System and Recording Medium,

One embodiment of the present invention is to acquire scrolls using a ship radar to acquire scrolls through sequential image processing such as grayscale conversion, Gaussian filter application, binarization, and labeling for tracing the scrolls in a radar image to implement an ARPA radar And a system for acquiring a table using the same.

A method of acquiring a target according to an embodiment of the present invention is a method of acquiring a target in a target tracking system using a ship radar, The sequential image processing technique may sequentially perform a gray level transformation process, a Gaussian filter filtering process, a binarization process, and a labeling process on the radar still image.

The radar still image includes geographical information such as a vessel or a vessel existing on the sea, geomorphic information such as a breakwater land island, noise information due to waves or rainfall, and a method of acquiring a ship using the ship radar, Noise, and geographical information such as land, breakwater, and the like, and then recognizing the information as a spot.

The filtering process using the Gaussian filter may include a standard deviation

(G (x)) of the Gaussian distribution mask can be obtained by adjusting the width of the Gaussian distribution mask through the value of the Gaussian distribution mask, .

x is the coordinate in the horizontal and vertical directions from the origin on the radar station.

Wherein the binarization process and the labeling process are performed by detecting a binarization value and a label value of a target pixel to be subjected to labeling conversion and a neighboring pixel adjacent to the target pixel in a pixel of the binarized image and then assigning a label value to the target pixel, If a label value is not assigned to all surrounding pixels, a new label value is given to a target pixel, and when a label value is given to the surrounding pixel, the same label value is given to the target pixel, Can be used to determine the acquisition target.

According to another aspect of the present invention, there is provided a system for acquiring a timetable using a method for acquiring timetables using a ship radar, the system comprising: An image converting unit for extracting a target image using an image processing technique, and performing grayscale conversion on the radar still image; A filtering unit for filtering an image output from the image converting unit using a Gaussian filter; And a binarization and labeling unit for binarizing and labeling the image filtered by the filtering unit.

Since the method of acquiring a table using a ship radar and the acquisition system of a table using the ship radar according to an embodiment of the present invention uses a labeling technique as an image processing technique for obtaining a table in a radar image for implementing an ARPA radar, It is possible to acquire an efficient map by applying features of image and characteristics of ship behavior.

FIG. 1 is a diagram illustrating a method for tracking a target according to the prior art.
2 is a flowchart showing a method of acquiring a target according to an embodiment of the present invention.
3A and 3B are diagrams illustrating a labeling process and a label size calculation process in FIG.
FIG. 4 is a diagram illustrating an approaching area search technique used after acquisition of a table in FIG.
Fig. 5 is a diagram showing a table obtained through Fig. 2. Fig.
FIG. 6 is a view showing a radar image and tracking results obtained through FIG. 2. FIG.
FIGS. 7A and 7B are graphs showing movement distances of the second target table through FIG. 4, and observations and predicted values calculated therefrom.
FIG. 8 is a view showing an example of a PC screen displaying a process of acquiring and tracking a target.
9 is a block diagram schematically showing a system for acquiring a target according to another embodiment of the present invention.
10 is a circuit diagram showing a power saving section of the system for acquiring a target of the present invention.

The terms used in this specification will be briefly described and the present invention will be described in detail.

While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments. Also, in certain cases, there may be a term selected arbitrarily by the applicant, in which case the meaning thereof will be described in detail in the description of the corresponding invention. Therefore, the term used in the present invention should be defined based on the meaning of the term, not on the name of a simple term, but on the entire contents of the present invention.

When an element is referred to as "including" an element throughout the specification, it is to be understood that the element may include other elements as well, without departing from the spirit or scope of the present invention. Also, the terms "part," " module, "and the like described in the specification mean units for processing at least one function or operation, which may be implemented in hardware or software or a combination of hardware and software .

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

FIG. 2 is a flowchart illustrating a method of acquiring a target table according to an embodiment of the present invention. FIGS. 3A and 3B illustrate a labeling process and a label size calculation process in FIG. FIG. 6 is a view showing a radar image obtained with reference to FIG. 2 and a tracking result, and FIGS. 7A to 7C are diagrams showing a radar image obtained by the method of FIG. FIG. 8 is a graph showing an example of a PC screen displaying a process of acquiring and tracking a target, and FIG. 9B is a view showing an example of a PC screen displaying a moving distance of the second target table through FIG. Is a block diagram schematically illustrating a system for acquiring a target according to another embodiment of the present invention.

Referring to FIG. 2, a method of acquiring a timetable according to an embodiment of the present invention includes acquiring a timetable in a timetable tracking system using a ship radar, The sequential image processing technique includes a grayscale conversion process S10, a filtering process S20 using a Gaussian filter, a binarization process S30, and a labeling process S40 ) Can be sequentially performed.

First, in the present invention, it is assumed that the object to be acquired is moved at a stop or a constant velocity.

The sequential image processing technique used to acquire the table may sequentially perform a grayscale conversion process, a Gaussian filter filtering process, a binarization process, and a labeling process.

The first radar still image may include geographical information such as a vessel or ship existing on the sea, a landmark such as a breakwater land island, and noise information due to waves or rainfall.

Accordingly, the method of acquiring a ship using the ship radar is a process of distinguishing and removing geographical information such as noise, land, and breakwater from a first radar still image, and recognizing it as a ship.

A step of forming a second radar still image continuous to the first radar still image by the ship radar after the process of acquiring such a timetable, a step of forming a second radar still image in the second radar still image, Estimating a position of the second target table by removing a noise around the observed second target table using a Kalman filter, and estimating a position of the second target table corresponding to the second target table, It is possible to track the trajectory through the process of analyzing the behavior of the second timetable by calculating the true velocity and the true azimuth value using the position.

In this case, in order to analyze the relationship between the first and second schedules in the image frame formed by the ship radar, the first area of the first radar still image is used as the second radar still image It is possible to distinguish which of the objects in the first object is the first object.

That is, in the proximity area search scheme, assuming that the first target table can be moved in any direction and does not deviate from the maximum speed, a target table in a certain range having the maximum movement distance as a radius in the second radar still image, It can be recognized as a second timetable corresponding to the timetable.

At this time, the maximum moving distance d can be calculated by the following equation (1).

[Equation 1]

d = (v / 60) * (D / 2r)

Where d is the maximum travel distance, v is the vehicle speed, D is the screen size (ie, pixel) of the ship radar, and R is the range of the ship radar.

[0030] The track acquisition system 10 implementing the method of acquiring the timetable according to the embodiment of the present invention configured as described above is configured to perform the steps (i.e., the first step (S10) to the fourth step (S40) A filtering unit 112, and a binarization and labeling unit 113 so as to correspond to the image processing unit 111, At this time, the image converting unit 111, the filtering unit 112, and the binarization and labeling unit 113 may be included in the ship radar by being algorithmized.

In other words, as shown in FIG. 9, the track acquisition system 10 extracts a track using a predetermined sequential image processing technique in a radar still image formed by a ship radar, and performs grayscale conversion on the radar still image A filtering unit 112 for filtering the image output from the image converting unit 111 by using a Gaussian filter, and a binarizing unit for binarizing the filtered image by the filtering unit 112, And a labeling unit 113.

According to the method of acquiring the target table and the system using the same according to the embodiment of the present invention configured as described above, since the labeling technique is used as the image processing technique for acquiring the target in the radar image for realizing the ARPA radar, It is possible to acquire efficient timber using characteristics and characteristics of ship behavior.

Hereinafter, a process of acquiring and tracing a trajectory by using a method of acquiring a trajectory according to an embodiment of the present invention and analyzing the behavior of the trajectory using the system will be described in more detail.

Generally, in the field of CCTV image processing, a target is referred to as a target, and a search for a meaningful object in an image is called an extraction. In the present invention, a term commonly used in the marine field, Acquisition is defined as acquiring the process of finding the target track to be tracked, and the process of analyzing the behavior of the target is defined as tracking.

The main function of the maritime radar is location verification and collision avoidance. The main concern in the present invention is the analysis of the surrounding traffic for collision avoidance. In other words, the main interest is the navigation sign such as a ship navigating the periphery and a navigation sign present in the sea. In the present invention, it is targeted to acquire and trace the surrounding traffic except the feature. The difficulty of tracking the target in the image is often due to the non-linear behavior of the target, which is difficult to model. In the present invention, it is assumed that the target (vessel or route marker) Explain.

In addition, the tracking of a target in a radar image for realizing an ARPA radar can be implemented by various methods. In the present invention, a target is acquired through sequential image processing such as grayscale conversion, Gaussian filter application, binarization and labeling, (Hereinafter referred to as NAS), and a method of analyzing the behavior through a Kalman filter having a system model of a constant velocity motion will be described.

Acquisition and tracking of marks

Radar still images include geographical information such as water and ship-like objects on the sea surface, land islands such as breakwaters, and noise information due to waves or rainfall.

In the present invention, the term "acquisition of a table" refers to a process of distinguishing and removing geographical information such as noise, land, and breakwater on a radar station, and recognizing the information as a table. It means the process of analyzing the behavior of the target by comparing with the target information.

Typically, a target tracking method in a CCTV image is to analyze a batch of color values in an image such as CamShift and MeanShift to track out the meaningful bundle and track where the bundle of color values moves (John G. In the present invention, the size and position information of a target are extracted through grayscale conversion, Gaussian filter application, binarization, and labeling, and the result is displayed in a next image frame through Nearest Area Search You can track the movement of successive scrolls. This method is disadvantageous in that it is not easy to acquire and track because the overlapping schedules can not be distinguished when a plurality of schedules overlap each other in an area where traffic is concentrated. In the present invention, except for this exceptional case .

In the present invention, grayscale conversion, Gaussian smoothing filter application, binarization, and labeling (WILLIAM K. PRATT (1999)) can be used as a method of extracting a target from an image. , 2001), and extracts the target using a series of sequential image processing techniques. The reason for applying the Gaussian filter is to integrate the noise around the target into the target, so that one target is not separated into several targets. The width of the Gaussian distribution mask is the standard deviation

However,

When the value is increased, the small sized scrolls close to one another are recognized as a single scrolled scroll, or the scrolled scrolls are bundled together. The binarization threshold is also used as a criterion for eliminating weak signal values, which is related to the detection capability of the ARPA radar, suggesting that it is necessary to determine appropriate values and binarization thresholds through the sensitivity analysis later.

In the present invention, a labeling step is used to filter out the topographic features such as land, islands, breakwaters, and the like to the size of the target to remove noise from the labeled objects. At this time, the size of the label means the number of adjacent pixels, and an example of calculating the size of the label is shown in Fig. 3B.

In addition, due to the nature of ship navigation radar, there is always a sea surface reflection wave, so it can be regarded as a noise of a certain size or less. However, by the solid line experiment, the noise is separated from the meaningful signal, do.

In order to track the target, it is important to analyze the correlation between the target images in the successive image frames. The target selection and tracking algorithms are variously based on the probability-based method and the Kalman filter-based method. In the present invention, (NAS), Nearest Area Search (NAS), and so on.

More specifically, when one tag is recognized as shown in A of FIG. 4, the NAS algorithm is a method of determining whether a tag among A's, A's, B's, A's is A, Assuming that A can move in any direction and does not deviate from the maximum speed, it is a method of recognizing A, that is, A in a next scan, as a radius of a maximum movement distance d per scan. At this time, if B or C is within the range and there are several indexes, A is recognized as a closer index.

In FIG. 4, d is determined by the line speed. As shown in Equation 1 (d = (v / 60) * (D / 2r)), the radial velocity is set to 60 kts, the radar range is set to 6 miles pixel, the moving speed per minute is 100 pixels / min, which is 1.7 pixels per second, and the normal scanning speed is 3 seconds, so the moving speed per scan is 5 pixels / scan.

In this embodiment, although the high-speed table can be obtained by setting d to 100 pixels, if the value is large, there may be an error in recognizing the smallest correlation table as the same table. Therefore, sensitivity analysis for acquisition and tracking improvement according to the change of d value is needed.

As shown in FIG. 5, according to IEC62388 specification of Table 1, the minimum number of traces of the ARPA radar is 40, and the maximum calculation amount of the table comparison algorithm (see FIG. 4 right)

, And even if the number of targets is maximum 160, there is no discontinuity in the screen display.

Here, in FIG. 5, a red square is the obtained map, and a yellow diagonal line indicates a map in which the distance bearing is calculated by the Kalman filter.

Sensitivity analysis

The performance of the acquisition of the target varies depending on the value of the Gaussian filter, the binarization threshold, the system noise Q of the Kalman filter, and the change of the observation noise R. In this embodiment, the resolution of the moderate sea (Beaufort Scale = 3) The value is 3.2 and the binarization threshold is 35, which is the easiest value to acquire the target in the state.

At this time, the value (G (x)) of the Gaussian filter is expressed by Equation (2).

&Quot; (2) "

On the other hand, in case of extending to a nonlinear model such as rapid turn / deceleration / acceleration, it is expected that the sensitivity change due to change of observation noise (R) and system noise (Q) As the weather changes

Value and a binarization threshold value.

Analysis of the behavior of scales

When labeling and tracking the trajectory by the NAS, the tracked position corresponds to the observed value of the Kalman filter and includes the system error and the observation error. The conventional track tracking method is to detect and track the trajectory by using Kalman filter to estimate the position in the next image (Byeong-Man Kim et al., 2005, Yanan Xu, 2013, etc.) In the invention, the tracking is performed by the NAS and the correct behavior of the target can be analyzed by eliminating the error with the Kalman filter as shown in Equation (3).

&Quot; (3) "

In the present invention, since the system model is assumed to be an object moving at a constant velocity, the basic system model of the Kalman filter is expressed by Equation (4)

&Quot; (4) "

If the state variable is held in the position and direction of the horizontal and vertical axes as shown in Equation (5)

&Quot; (5) "

The coefficient matrices A and H of the system model are derived as shown in Equation (6).

&Quot; (6) "

This expresses the constant velocity model of Equation (7).

&Quot; (7) "

As described above, the system model is defined from Equation (4) to Equation (7), and the system noise Q and the observation noise R value are defined as Equation (8).

&Quot; (8) "

The difference between the method of tracking a track using an existing Kalman filter and the method of tracking a track in the present invention is that the position information observed by the NAS is removed using a Kalman filter as shown in FIG. , And the calculated predicted value is used to calculate the true velocity and the true azimuth of the target. At this time, it is obvious that removing the noise using the Kalman filter is closer to the true position than calculating the observations as it is because the behavior of the target includes noise.

As a result, the result of the track tracing in the radar image simulated with the bit map as shown in FIG. 6 is shown, and the difference between the observed value and the predicted value can be shown in a chart as in FIGS. 7A to 7B. According to this, when the two scrolls overlapped, they showed chase of chase, but when they got out of the chase, they would resume tracking properly.

In addition, the observations in FIGS. 7A to 7B indicate the position of the target table obtained through image processing in the radar image, and the predicted value is a value obtained by applying a Kalman filter of the constant velocity system model to the position of the observed target table. In the present invention, these predictions can be used to calculate the true path and the true azimuth of the behaviorally interpreted object.

Solid line test result

As described above,

And binarization threshold, and the size range of the label that is the basis of feature removal and noise removal are determined through simulation. Therefore, it is possible to confirm that the ARPA module developed through the solid line test works well in the real sea. Also, as shown in FIG. 8, the spoke signal is received from the FMCW radar, is expressed on a PC through predetermined software, and the spoke signal is loaded on a solid line to verify the performance of the ARPA while operating in a real sea area.

As a result, the actual sea area operation result and the binarization threshold were related to the image complexity. The size range of the label depends on the sea clutter and sensitivity depending on the weather condition. In 2-6 mile range, 70- 700, and 50-500 for more than 6 miles. Also,

And binarization thresholds, we found that it is appropriate to use a value of 3.2 and a binarization threshold of 35 as in the simulation.

The real line test area is the sea near Oryukdo, and the speed and direction of the ship are calculated for merchant vessels and cruise ships that pass around, and up to 20 vessels are detected. It was confirmed that a small ship of less than 10 meters in length was obtained well within 6 miles.

As described above, the acquisition and tracking system of the present invention has a function of acquiring and tracking a utility that can be practically used by using an ARPA radar, and includes a labeling technique for acquisition of a target table, a NAS technique for tracking a target, The new approach of applying Kalman filter to the behavior analysis makes practical and commercialization possible.

On the other hand, a coating layer coated with a composition for anti-fouling coating is formed on the surface of the ship radar used in the system for obtaining the target object so as to effectively prevent and remove the adhesion of the fouling material. The composition for antifouling coating contains boric acid and sodium carbonate in a molar ratio of 1: 0.01 to 1: 2, and the total content of boric acid and sodium carbonate is 1 to 10% by weight based on the total aqueous solution. In addition, sodium carbonate or calcium carbonate may be used as the material for improving the coating property of the coating layer, but sodium carbonate is preferably used. The molar ratio of boric acid to sodium carbonate is preferably 1: 0.01 to 1: 2. If the molar ratio is out of the above range, the coating property of the substrate may be decreased or the moisture adsorption on the surface of the coating may increase.

The boric acid and sodium carbonate are preferably used in an amount of 1 to 10% by weight based on the total weight of the composition. When the amount is less than 1% by weight, the coating properties of the base material deteriorate. When the amount exceeds 10% by weight, easy to do.

On the other hand, as a method of coating the composition for antifouling coating on a substrate, it is preferable to coat it by a spray method. The thickness of the final coating film on the substrate is preferably 500 to 2000 angstroms, and more preferably 1000 to 2000 angstroms. When the thickness of the coating film is less than 500 ANGSTROM, there is a problem that it deteriorates in the case of a high-temperature heat treatment. When the thickness of the coating film is more than 2000 ANGSTROM, crystallization of the coating surface tends to occur.

Further, the composition for antifouling coating may be prepared by adding 0.1 mol of boric acid and 0.05 mol of sodium carbonate to 1000 mL of distilled water and then stirring.

Also, the image converting unit 111, the filtering unit 112, the binarization and labeling unit 113 may be accommodated in one case and protected. At this time, a sound-absorbing layer (not shown) for noise reduction may be applied to the inside of the case. The nonwoven fabric constituting the sound-absorbing layer may be a needle punch nonwoven fabric. The types of the fibers constituting the sound-absorbing layer made of the needle punch nonwoven fabric include polyester fibers, nylon fibers, polypropylene fibers, acrylic fibers, and natural fibers.

The thickness of the sound-absorbing layer is preferably 0.3 to 15 mm. If the thickness of the sound-absorbing layer is less than 0.3 mm, a sufficient sound-absorbing effect can not be obtained. If the thickness of the sound-absorbing layer is more than 15 mm, the height of the case is reduced.

The unit weight of the sound-absorbing layer is preferably 10 to 1000 g / m 2. If it is less than 10 g / m < 2 >, a sufficient sound absorption effect can not be obtained, and if it exceeds 1000 g / m < 2 >

The fineness of the fibers constituting the sound-absorbing layer is preferably in the range of 0.1 to 30 decitex. If it is less than 0.1 decitex, absorption of low-frequency noise is difficult and cushioning property is lowered, which is not preferable. Further, if it exceeds 30 decitex, it is not preferable to absorb high frequency noise. Among these, the fineness of the fibers constituting the sound-absorbing layer is more preferably in the range of 0.1 to 15 decitex.

Since the sound-absorbing layer is provided inside the case, noise during operation of the image converting unit 111, the filtering unit 112, and the binarizing and labeling unit 113, which are protected inside the case, can be reduced.

10 is a circuit diagram showing a power saving section of the system for acquiring a target of the present invention.

The power saving unit of the present invention includes a plurality of voltage supply lines for respectively transmitting voltages from an input terminal to a plurality of output terminals, a plurality of control switches respectively connected between the voltage supply line and the ground voltage terminal, A plurality of voltage dividing resistors connected to each other and a plurality of application circuit sections receiving a voltage through an output terminal and an application circuit section performing an algorithm operation and monitoring a change in a voltage applied to the voltage dividing resistor, And a microprocessor for adjusting the switching frequency and the duty ratio of the switch.

Further comprising a plurality of chokes connected between the input terminal and the control switch, respectively, and a plurality of capacitors connected in parallel with the control switch and the voltage dividing resistor between the control switch and the voltage dividing resistor, And further includes a plurality of reverse current prevention diodes connected.

The microprocessor further includes a plurality of feedback lines connected to the analog-to-digital converter of the microprocessor so as to monitor the voltage applied to the voltage-dividing resistor, Respectively.

In addition, a plurality of voltage supply lines for transmitting a voltage from an input terminal to a plurality of output terminals, respectively, a plurality of application circuit parts for receiving voltages through the output terminal, and a plurality of application circuit parts connected between the voltage supply line and the ground voltage terminal, And a control unit for controlling the voltage applied to the application circuit unit to be supplied with a constant voltage by monitoring the change in the voltage detected through the switch unit by executing the algorithm operation of the application circuit unit, And a microprocessor for controlling the switching of the negative part.

The present invention can provide a power saving unit capable of efficiently supplying power according to an application circuit and controlling an output voltage in a programmable manner by integrally controlling the power supply portion and the application circuit portion in one power management .

The power saving unit 130 of the present invention will now be described in detail with reference to FIG.

10, the power saving unit 130 according to the embodiment of the present invention includes first and second voltage supply lines VSL1 and VSL2, first and second control switches Q1 and Q2, And second and third voltage dividing resistors R1, R2, R3 and R4, first and second application circuit portions 141 and 142, and a microprocessor 150. In addition, the power saving unit 130 includes the first and second choke coils L1 and L2, the first and second capacitors C1 and C2, the first and second reverse current diodes D1 and D2, 1 and second voltage feedback lines FBL1 and FBL2.

The power saving unit 130 according to the embodiment of the present invention includes one switch unit in one application circuit unit. Hereinafter, for convenience of explanation, a power saving unit including two application circuit units and two switch units will be described as an example. However, an application circuit unit may be added as needed, and a switch unit may be additionally provided. The switch unit may mean one unit consisting of a voltage supply line, a control switch, a voltage dividing resistor, a voltage feedback line, a choke coil, a capacitor, and a reverse current prevention diode.

The first voltage supply line VSL1 may be branched from the voltage input VCC_IN and connected to the first voltage output VCC_OUT1 to transmit a voltage input through the voltage input VCC_IN to the first voltage output VCC_OUT1 .

The first control switch Q1 is connected between the first voltage supply line VSL1 and the ground voltage terminal, and the switching operation can be controlled by the microprocessor 150. [

The first voltage-dividing resistors R1 and R2 are connected between the first voltage supply line VSL1 and the ground voltage terminal at the rear end of the first control switch Q1 and connected in parallel with the first control switch Q1 .

The first application circuit part 141 may receive the input voltage through the first voltage output terminal VCC_OUT1. The first application circuit unit 141 can perform the algorithm operation by the microprocessor 150 and transmit the information about the voltage required for the first application circuit unit 141 to the microprocessor 150. [

The microprocessor 150 feeds back the voltage detected through the first voltage dividing resistors R1 and R2 through the first voltage feedback line FBL1 and outputs the fed analog value to the internal ADC1 Converter), and can monitor the change of the converted digital value. Here, the digital value may refer to a supply voltage of the first application circuit 141 that the microprocessor 150 can recognize. The microprocessor 150 may sense a change in the digital value and adjust the switching frequency and the duty ratio of the first control switch Q1 so that the first application circuit unit 141 is supplied with a constant voltage.

The first choke coil L1 is connected on the first voltage supply line VSL1 and more specifically may be connected between the branch node of the voltage input VCC_IN and the first control switch Q1.

The first capacitor C1 may be connected in parallel with the first control switch Q1 and the first voltage dividing resistors R1 and R2 between the first control switch Q1 and the first voltage dividing resistors R1 and R2.

The first reverse current prevention diode D1 is connected on the first voltage supply line VSL1 and more specifically may be connected between the first control switch Q1 and the first capacitor C1.

The first voltage feedback line FBL1 may be connected between the first and second voltage dividing resistors R1 and R2 to feedback the voltage applied to the first voltage dividing resistors R1 and R2 to the microprocessor 150. [

The second voltage supply line VSL2 may be branched from the voltage input VCC_IN and connected to the second voltage output VCC_OUT2 to transfer the voltage input through the voltage input VCC_IN to the second voltage output VCC_OUT2 .

The second control switch Q2 is connected between the second voltage supply line VSL2 and the ground voltage terminal, and the switching operation can be controlled by the microprocessor 150. [

The second voltage-dividing resistors R3 and R4 are connected between the second voltage supply line VSL2 and the ground voltage terminal at the rear end of the second control switch Q2 and may be connected in parallel with the second control switch Q2.

The second application circuit part 142 may receive the input voltage through the second voltage output terminal VCC_OUT2. The second application circuit unit 142 can perform the algorithm operation by the microprocessor 150 and transmit the information about the voltage required for the second application circuit unit 142 to the microprocessor 150. [

The microprocessor 150 feeds back the voltage detected through the second voltage dividing resistors R3 and R4 through the second voltage feedback line FBL2 and outputs the analog value fed back to the internal ADC2 Convertor), and monitor the change of the converted digital value. Here, the digital value may mean the supply voltage of the second application circuit part 142 that the microprocessor 150 can recognize. The microprocessor 150 may sense the change of the digital value and adjust the switching frequency and the duty ratio of the second control switch Q2 so that the constant voltage is supplied to the second application circuit part 142. [

The second choke coil L2 is connected on the second voltage supply line VSL2 and more specifically may be connected between the branch node of the voltage input VCC_IN and the second control switch Q2.

The second capacitor C2 may be connected in parallel with the second control switch Q2 and the second voltage dividing resistors R3 and R4 between the second control switch Q2 and the second voltage dividing resistors R3 and R4.

The second reverse current prevention diode D2 is connected on the second voltage supply line VSL2 and more specifically may be connected between the second control switch Q2 and the second capacitor C2.

The second voltage feedback line FBL2 may be connected between the second voltage dividing resistors R3 and R4 to feed back the voltage applied to the second voltage dividing resistors R3 and R4 to the microprocessor 150. [

In the above description, the system composed of two application circuit portions and two switch portions has been described as an example. However, the present invention is not limited to such a configuration, and additional configuration of the switch portion according to addition of application circuit portions is also possible.

The present invention is not limited to the above-described embodiment, but may be embodied in the following claims. The present invention is not limited to the above- It will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

111: image conversion unit 112: filtering unit
113: binarization and labeling section

Claims

A method for acquiring a target in a track tracking system using a ship radar,
The method of claim 1, further comprising the steps of: extracting a target from a radar still image formed by the ship radar using a predetermined sequential image processing technique,
Wherein the sequential image processing technique sequentially performs a grayscale conversion process, a Gaussian filter process, a binarization process, and a labeling process on the radar still image.

The method according to claim 1,
The radar still image includes geographical information such as a ship or ship existing on the sea, a landmark such as a breakwater land island, noise information due to waves or rainfall,
Wherein the method for acquiring a ship using the ship radar is a process for distinguishing and removing geographical information such as noise, land, and breakwater from the radar still image, and recognizing it as a ship.

The method according to claim 1,
The filtering process using the Gaussian filter
Standard Deviation

The width of the Gaussian distribution mask is adjusted so that the adjacent small scrolls can be recognized as a single scrolled scroll, or the scrolled scrolls can be grouped together,
Wherein the value G (x) of the Gaussian filter is determined by the following equation.

The method according to claim 1,
Wherein the binarization process and the labeling process are performed by detecting a binarization value and a label value of a target pixel to be subjected to labeling conversion and a neighboring pixel adjacent to the target pixel in a pixel of the binarized image and then assigning a label value to the target pixel, If a label value is not assigned to all surrounding pixels, a new label value is given to a target pixel, and when a label value is given to the surrounding pixel, the same label value is given to the target pixel, Wherein the object to be acquired is determined using the statistical calculation value of the vessel radar.

A system for acquiring a target by using a method of acquiring a target using a ship radar according to any one of claims 1 to 4,
The system
The method of claim 1, further comprising the steps of: extracting a target from a radar still image formed by the ship radar using a predetermined sequential image processing technique,
An image converter for performing gray level transformation on the radar still image;
A filtering unit for filtering an image output from the image converting unit using a Gaussian filter; And
And a binarization and labeling unit for binarizing the image filtered by the filtering unit and labeling the filtered image.